1. Technical Field
The present invention relates to a data processing system. In particular, the present invention relates to modeling clustering technologies in a data center. Still more particularly, the present invention relates to providing a common cluster model for configuring, managing, and operating different clustering technologies in a data center.
2. Description of Related Art
In a data center, a variety of clustering technologies may be used to configure, manage, and operate clusters. Examples of these clustering technologies include Veritas™ clustering server, which is available from Veritas Software Corporation, Tivoli® System Automation (TSA) for Multiplatforms and WebSphere, which is available from International Business Machines Corporation, and Microsoft® cluster server available from Microsoft Corporation.
These clustering technologies each have their own way to configure, manage, and operate clusters. For example, Veritas clustering server provides a configuration definition template for configuring a Veritas cluster. TSA cluster uses a pre-camp policy and a CLI cluster command to configure the TSA cluster. Microsoft cluster server uses configuration user interface to configure the cluster and encapsulate all detail configuration steps from the users. The WebSphere cluster uses a deployment manager server to configure a WebSphere Application Server (WAS) cluster by calling a WAS internal application programming interface (API). With the variety of ways to configure, manage, and operate cluster, no mechanism is present that allows users to easily interact with different clustering technologies.
In addition, these clustering technologies each have their own data model for modeling the cluster. When encountering different clustering technologies, users have to have knowledge of different data models associated with each of these clustering technologies. For example, if both a WAS cluster and a TSA high availability cluster are used in a data center, a user must handle these two clustering technologies and their data models separately.
Therefore, it would be advantageous to have an improved method that operates different clustering technologies and understands different data models.